Weaviate vs Chroma

Enterprise Design

Weaviate is built as a complete database system with production features like replication, sharding, and monitoring built-in from the start.

Core Components

• • HNSW vector index
• • Schema-based data model
• • GraphQL query engine
• • Module ecosystem

Minimalist Design

Chroma prioritizes developer experience with a simple embedded database that requires zero configuration and minimal setup.

Core Components

• • HNSW index for vectors
• • SQLite/DuckDB backend
• • Collection-based API
• • Automatic persistence

Getting Started

import weaviate

client = weaviate.Client("http://localhost:8080")

# Create schema
class_obj = {
    "class": "Article",
    "properties": [{
        "name": "content",
        "dataType": ["text"]
    }]
}
client.schema.create_class(class_obj)

# Add data
client.data_object.create(
    data_object={"content": "..."},
    class_name="Article",
    vector=embedding
)

Pros

• ✓ Rich query capabilities
• ✓ GraphQL flexibility
• ✓ Production features
• ✓ Module ecosystem

Cons

• ✗ Complex schema setup
• ✗ Steeper learning curve
• ✗ More boilerplate

Getting Started

import chromadb

# Create client and collection
client = chromadb.Client()
collection = client.create_collection("articles")

# Add documents
collection.add(
    documents=["..."],
    embeddings=[embedding],
    ids=["doc1"]
)

# Query
results = collection.query(
    query_embeddings=[query_vector],
    n_results=10
)

Pros

• ✓ Intuitive API
• ✓ Zero configuration
• ✓ Quick prototyping
• ✓ Minimal boilerplate

Cons

• ✗ Limited query options
• ✗ Basic features only
• ✗ No advanced APIs